Introduction

In part 1 of this article we covered the importance of quality assurance of UT data, that is, understanding for each particular application, the accuracy required of the UT data, and new ways/graphical program to analyze and show the interrelationships of data by location for trending. Part 1 included:

UT Data Reporting and Evaluation

Imaging UT Data

Evaluating the Quality of Static UT Data

Visual Trending of UT data

Mathematical Trending of UT Data

Now, in Part 2, we will cover data quality issue statistics and possible sources of poor quality UT data.

Results From Evaluating and Trending Recovery Boiler UT Data

After fifteen years of analyzing the UT data from more than 390 recovery boiler inspections we have learned that, on the average, 25% of the UT readings for a given boiler inspection are inconsistent or inaccurate. This means the UT readings do not “make sense”. example, the UT readings have a significant difference in thickness even though they came from the same section of the boiler during the same inspection. Another example, is the tube getting thicker over time. This finding does not mean UT data is of no use. It simply points out that the utilization of UT in the field, is not as accurate as industrial America has been led to believe. (Cher, use this shaded area for a pull quote)

Factors Affecting The Quality of UT Data

Unfortunately, there is more than one reason for unacceptable quality of UT data. Here are a few, which is by no means all inclusive, factors that TCRI has witnessed as consistently having a significant impact on the quality of UT data.